knitr::opts_chunk$set(echo = FALSE, message = FALSE)
library(Seurat)
library(ggplot2)
library(data.table)
library(MAST)
library(SingleR)
library(dplyr)
library(tidyr)
library(limma)
library(scRNAseq)## R version 4.0.2 (2020-06-22)
## Platform: x86_64-apple-darwin17.0 (64-bit)
## Running under: macOS Catalina 10.15.4
##
## Matrix products: default
## BLAS: /Library/Frameworks/R.framework/Versions/4.0/Resources/lib/libRblas.dylib
## LAPACK: /Library/Frameworks/R.framework/Versions/4.0/Resources/lib/libRlapack.dylib
##
## locale:
## [1] en_US.UTF-8/en_US.UTF-8/en_US.UTF-8/C/en_US.UTF-8/en_US.UTF-8
##
## attached base packages:
## [1] parallel stats4 stats graphics grDevices utils datasets
## [8] methods base
##
## other attached packages:
## [1] scRNAseq_2.2.0 limma_3.44.3
## [3] tidyr_1.1.1 dplyr_1.0.2
## [5] SingleR_1.2.4 MAST_1.14.0
## [7] SingleCellExperiment_1.10.1 SummarizedExperiment_1.18.2
## [9] DelayedArray_0.14.1 matrixStats_0.56.0
## [11] Biobase_2.48.0 GenomicRanges_1.40.0
## [13] GenomeInfoDb_1.24.2 IRanges_2.22.2
## [15] S4Vectors_0.26.1 BiocGenerics_0.34.0
## [17] data.table_1.13.0 ggplot2_3.3.2
## [19] Seurat_3.2.0
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## loaded via a namespace (and not attached):
## [1] AnnotationHub_2.20.1 BiocFileCache_1.12.1
## [3] plyr_1.8.6 igraph_1.2.5
## [5] lazyeval_0.2.2 splines_4.0.2
## [7] BiocParallel_1.22.0 listenv_0.8.0
## [9] digest_0.6.25 htmltools_0.5.0
## [11] magrittr_1.5 memoise_1.1.0
## [13] tensor_1.5 cluster_2.1.0
## [15] ROCR_1.0-11 globals_0.12.5
## [17] colorspace_1.4-1 blob_1.2.1
## [19] rappdirs_0.3.1 ggrepel_0.8.2
## [21] xfun_0.16 crayon_1.3.4
## [23] RCurl_1.98-1.2 jsonlite_1.7.0
## [25] spatstat_1.64-1 spatstat.data_1.4-3
## [27] survival_3.2-3 zoo_1.8-8
## [29] ape_5.4-1 glue_1.4.1
## [31] polyclip_1.10-0 gtable_0.3.0
## [33] zlibbioc_1.34.0 XVector_0.28.0
## [35] leiden_0.3.3 BiocSingular_1.4.0
## [37] future.apply_1.6.0 abind_1.4-5
## [39] scales_1.1.1 DBI_1.1.0
## [41] miniUI_0.1.1.1 Rcpp_1.0.5
## [43] viridisLite_0.3.0 xtable_1.8-4
## [45] reticulate_1.16 bit_4.0.4
## [47] rsvd_1.0.3 htmlwidgets_1.5.1
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## [53] pkgconfig_2.0.3 uwot_0.1.8
## [55] dbplyr_1.4.4 deldir_0.1-28
## [57] tidyselect_1.1.0 rlang_0.4.7
## [59] reshape2_1.4.4 later_1.1.0.1
## [61] AnnotationDbi_1.50.3 munsell_0.5.0
## [63] BiocVersion_3.11.1 tools_4.0.2
## [65] generics_0.0.2 RSQLite_2.2.0
## [67] ExperimentHub_1.14.1 ggridges_0.5.2
## [69] evaluate_0.14 stringr_1.4.0
## [71] fastmap_1.0.1 yaml_2.2.1
## [73] goftest_1.2-2 knitr_1.29
## [75] bit64_4.0.2 fitdistrplus_1.1-1
## [77] purrr_0.3.4 RANN_2.6.1
## [79] pbapply_1.4-3 future_1.18.0
## [81] nlme_3.1-148 mime_0.9
## [83] compiler_4.0.2 plotly_4.9.2.1
## [85] curl_4.3 png_0.1-7
## [87] interactiveDisplayBase_1.26.3 spatstat.utils_1.17-0
## [89] tibble_3.0.3 stringi_1.4.6
## [91] lattice_0.20-41 Matrix_1.2-18
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## [113] sctransform_0.2.1 GenomeInfoDbData_1.2.3
## [115] mgcv_1.8-31 grid_4.0.2
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## Warning: Using `as.character()` on a quosure is deprecated as of rlang 0.3.0.
## Please use `as_label()` or `as_name()` instead.
## This warning is displayed once per session.
In v2 of the analysis we decided to include the control mice from the Nbeal experiment with the Migr1 and Mpl mice. The thought is that it may be good to have another control, since the Migr1 control has irradiated and had a bone marrow transplantation. I’m going to split the Rmarkdown files into separate part, to better organize my analysis.
The previous cell labeling file was reaching a thousand lines and taking a while to knit, so I decided to do some extra analysis in a new markdown file.
Comparing both ?CMP/Neutro & Stem Cells (which seem to be either and HSPC or GMP population) just to the other granulocyte clusters.
## p_val avg_logFC pct.1 pct.2 p_val_adj
## Elane 0 3.493556 0.980 0.372 0
## Mpo 0 3.417640 0.975 0.267 0
## Ctsg 0 3.394861 0.989 0.262 0
## Prtn3 0 3.388360 1.000 0.465 0
## Npm1 0 2.323565 1.000 0.605 0
## Rps2 0 2.273400 1.000 0.852 0
## Ms4a3 0 2.239992 0.980 0.447 0
## Plac8 0 2.195841 1.000 0.798 0
## Ptma 0 2.049744 1.000 0.689 0
## Rpl3 0 1.976710 1.000 0.655 0
Many of the same genes we saw in the previous labeling document which indicate high expression of genes associated with neutrophil azurophilic granules.
## p_val avg_logFC pct.1 pct.2 p_val_adj
## Hist1h2ap 0 1.772091 0.874 0.310 0
## Ube2c 0 1.555801 0.911 0.344 0
## Mki67 0 1.554029 0.991 0.325 0
## Top2a 0 1.549254 0.950 0.284 0
## Stmn1 0 1.538331 0.997 0.350 0
## Tuba1b 0 1.477424 0.983 0.375 0
## Fcnb 0 1.473164 0.953 0.421 0
## Pclaf 0 1.469087 0.986 0.291 0
## Tubb5 0 1.457734 0.991 0.428 0
## Hist1h1b 0 1.457321 0.819 0.265 0
Many of these genes are being co-expressed in ?GMP and ?CMP/Neutro. Going to just compare those two clusters to see what we find.
## p_val avg_logFC pct.1 pct.2 p_val_adj
## Chil3 2.268234e-308 1.9961851 0.995 0.837 4.536467e-305
## BC100530 8.275933e-16 1.2908424 0.345 0.144 1.655187e-12
## Stfa1 1.048237e-17 1.1930080 0.425 0.203 2.096474e-14
## Zmpste24 2.680874e-77 0.9027657 0.920 0.811 5.361749e-74
## Orm1 1.123386e-80 0.7149312 0.747 0.237 2.246772e-77
## Inhba 3.290515e-126 0.7098089 0.884 0.315 6.581029e-123
## St13 1.530157e-192 -0.6987111 0.586 0.986 3.060313e-189
## Lyar 1.363107e-175 -0.7054569 0.532 0.961 2.726213e-172
## Dtymk 9.975117e-172 -0.7067991 0.890 1.000 1.995023e-168
## Igfbp4 5.816834e-139 -0.7073358 0.631 0.994 1.163367e-135
## [1] "Most Up-Regulated Markers"
## p_val avg_logFC pct.1 pct.2 p_val_adj
## Jchain 1.650999e-158 6.495201 1.000 0.155 3.301997e-155
## Igha 3.038741e-69 6.359437 0.676 0.150 6.077482e-66
## Ighg2b 3.811623e-12 4.517413 0.243 0.059 7.623246e-09
## Ighg2c 6.141567e-08 4.390962 0.378 0.062 1.228313e-04
## Ighg1 6.492017e-07 4.273659 0.108 0.030 1.298403e-03
## Ighg3 6.441953e-55 3.945499 0.919 0.041 1.288391e-51
## [1] "Most Down-Regulated Markers"
## p_val avg_logFC pct.1 pct.2 p_val_adj
## Hmgb1 1.357366e-26 -2.297729 0.784 0.949 2.714732e-23
## Tmsb10 1.805625e-120 -2.341805 1.000 0.998 3.611251e-117
## Cd79b 6.726054e-58 -2.368993 0.919 0.995 1.345211e-54
## Ptma 3.254212e-100 -2.621752 1.000 0.999 6.508424e-97
## Vpreb3 4.150379e-75 -2.736629 1.000 0.985 8.300757e-72
## Ebf1 1.874495e-60 -3.321159 0.243 0.984 3.748991e-57
Ebf1 (early B-cell factor) is highly down-regulated in B-cell cluster 4, so makes it seem like a more mature B-cell.
Using the R&D Systems list of B cell markers and from link (the second one being more useful). I’m going to look to see how they’re distributed among the B cell clsuters).
## [1] "Cd34" "Ly6a" "Ly6d" "Ly6i" "Ly6k" "Ly6g6f" "Cd19"
## [1] "Ebf1" "Flt3" "Gfi1b" "Kitl" "Kit" "Ptprcap" "Pax5"
## [8] "Cd19"
Nothing conclusive from the above analysis. All were positive markers for B-cells except for Ly6
Seen in the UMAP Projection above, the teal color represents Megakaryocytes and it can be seen that the cluster is in two parts in the UMAP projection. Going to look at what is different from these two groups by subcluster MKs.
## Warning: The default method for RunUMAP has changed from calling Python UMAP via reticulate to the R-native UWOT using the cosine metric
## To use Python UMAP via reticulate, set umap.method to 'umap-learn' and metric to 'correlation'
## This message will be shown once per session